Zintus Engineer

Inspiration

AI coding tools are powerful, but most workflows still ask developers to trust a generated answer too quickly. We built Zintus Engineer because production software needs proof: exact scope, repeatable execution, test evidence, security review, audit trails, and a human decision before anything ships.

What it does

Zintus Engineer turns an AI coding request into a bounded, evidence-backed engineering workflow. AI proposes a plan and code changes, but Zintus ties the work to an exact commit, runs it through sandboxed execution, captures artifacts, verifies tests, checks risk, and presents the result for human review.

Zintus also includes a local-first BYOK AI router across many providers, with quota-aware routing, failover, privacy controls, cost-savings estimates, and web, desktop, mobile, CLI, and gateway surfaces.

How we built it

We built Zintus with a multi-agent engineering process using TERRA for planning, SOL for building, and LUNA for adversarial review. Across implementation, testing, repair loops, evaluation, and verification, we used roughly 3.9B tokens to harden the product and improve reliability.

Technically, Zintus is a TypeScript/Bun monorepo with a Next.js web app, Tauri desktop app, Expo mobile app, CLI, gateway service, shared provider/router packages, and a dedicated Engineer package.

Challenges we ran into

The hardest part was making AI autonomy bounded and reviewable. We had to avoid vague “agent did it” behavior and instead build deterministic contracts, durable timelines, isolated execution, artifact capture, independent verification, and human approval gates.

Accomplishments that we're proud of

We built a working cross-surface AI platform and an evidence-driven engineering workflow where AI can propose changes without silently taking final authority. We also filed patent-pending applications covering verifiable AI engineering workflows, bounded autonomous execution, and evidence-backed human review.

What we learned

The key lesson is that AI engineering needs systems, not just prompts. The useful product is not only generation; it is the proof layer around generation.

What's next for Zintus Engineer

Next we want to harden hosted judge workflows, improve production deployment, expand verification evidence, and make Zintus Engineer easier for teams to use on real repositories.

Built With

• actions
• agents
• ai
• bun
• developer
• docker
• expo.io
• github
• next.js
• openai
• software
• tauri
• testing
• tools
• typescript
• vercel